Coding Interviews at AI Companies

Many AI companies are shifting away from pure data structures and algorithms (DSA) questions toward assessments (i.e. practical coding rounds) that mirror actual job responsibilities. At the same time, traditional DSA questions are still part of many interview loops, and strong candidates effectively prepare for both.

We spoke with 50+ candidates from top AI companies to understand how these interviews actually run in practice.

Practical coding interview formats

Most notably, there are three main types of practice coding rounds:

Working in production codebases: Cursor gives candidates a full day working in their actual production codebase. Your task is to find something useful and ship it. LangChain uses large, undocumented codebases where you build real features or customer-facing demos.

Debugging and refactoring: Sierra AI asks candidates to debug existing customer-support agent flows or build new ones. OpenAI has candidates refactor 100+ lines of messy production-style code.

Building practical tools: ElevenLabs asks candidates to build dubbing tools, file-permission systems, and workflows that reflect actual product needs.

Traditional coding still matters

Classic data structures and algorithms remain common at OpenAI, Anthropic, xAI, Perplexity, and Sierra. Expect problems on graph and tree traversals, LRU cache implementations, and stack trace analysis.

System design questions often have an AI flavor. You might be asked about streaming systems, rate limiters, batch inference, P2P distribution for model binaries, or GPU utilization.

How AI company interviews differ

From our direct conversations with candidates, we've identified four key differences between AI company interviews and traditional tech interviews:

• Work simulation over pure algorithms: You'll face more realistic coding scenarios, though DSA rounds are still relevant .
• AI-native expectations: Even infrastructure roles require understanding LLMs, embeddings, RAG, and agents. Learn more about generative AI here
• Scale thinking: Interviewers repeatedly ask about 10x, 100x, and 1000x scale scenarios and GPU optimization.

How to prepare

You need solid DSA fundamentals, but don't spend weeks grinding problem sets. Instead, use our coding fast track to do targeted prep for coding interviews within 5 hours.

DSA is simply a passing bar at these companies. You'll need to perform in the practical coding interviews to stand out technically.

Here's how to prepare for practical coding rounds:

1. Find out what type of practical coding interview the company uses. Ask your recruiter or check with people who've interviewed there recently. Read interview experiences
2. Find an open source repository that's similar in scope to what you'll face. Look for projects with comparable complexity and tech stack.
3. Work in the codebase to implement a feature or refactor code. Match your task and time box to what the company asks for. If they give candidates an hour, practice with an hour.
4. Optionally, raise a pull request in the repository to get feedback on your approach and code quality.

If implementing a feature in an hour sounds daunting, remember that most companies expect you to use AI tools to work quickly. Success in these rounds isn't about memorizing syntax or knowing the optimal algorithm, but rather giving clear instructions to AI tools, executing your vision, and verifying the output.

What’s next

We’re building a hands-on Practical Coding Interview Course focused on real interview scenarios.

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